Machine Learning with R Training Course

  Module-1 Data Science with R
Live Lecture 
·       Exploratory Data Analysis and Visualization

·       R for Data Science

·       Data Mining

·       Data Analysis for Evidence Based Decision Making

·       Industry Applications of Advanced Analytics Models

·       Big Data Analytics with Spark

·       Project Management in Analytics

·       Information to Insight

·       Career Management

  Module-2 Introduction to Machine Learning
Live Lecture 
·       An Introduction

·       The Regression Algorithms

·       The Classifiers: Bayesian and kNN

·       Tree Based Algorithms

·       SVM and Improving Performance

  Module-3 Random Forest
Live Lecture 
·       Single Decision Tree

·       Rise of Ensemble Method

·       Practical Exercises in R on Business Case Studies

  Module-4 General Boosting & Bagging
Live Lecture 
·       Decision Tree Ensembles: Bagging and Boosting

·       The Case Study: Analysis of Credit Data

·       The Case Study: The Titanic Accident

·       The Case Study: Comparing Algorithms

  Module-5 Support Vector Machines
Live Lecture 
·       Introduction to the Support Vector Machines

  Module-6 Neural Networks
Live Lecture 
·       An Introduction

·       The Perceptron learning procedure

·       The backpropagation learning procedure

·       Learning feature vectors for words

·       Object recognition with neural nets

·       Optimization: How to make the learning go faster

·       Recurrent neural networks

·       More recurrent neural networks

·       Ways to make neural networks generalize better

·       Combining multiple neural networks to improve generalization

·       Hopfield nets and Boltzmann machines

·       Restricted Boltzmann machines (RBMs)

·       Stacking RBMs to make Deep Belief Nets

·       Deep neural nets with generative pre-training

·       Modeling hierarchical structure with neural nets

·       Recent applications of deep neural nets

  Module-7 Text Mining with R
Live Lecture 
·       An Introduction to the Text Mining

·       TM Packages in R

·       Regular Expressions

·       Sentiment Analysis

·       Topic Modelling

·       Network Analysis

·       Clustering